Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images
Viet Hung Duong, Duc Quyen Nguyen, Thien Van Luong, Huan Vu, Tien Cuong Nguyen
Abstract
In recent years, traffic surveillance systems have begun leveraging fisheye lenses to minimize the requisite number of cameras for comprehensive coverage of streets and intersections. However, as fisheye images have large radial distortion, they pose new challenges to standard object detection algorithms. In this study, we propose a robust object detection method in traffic scenarios using fisheye cameras. Specifically, we develop a novel data augmentation method, which is applied to VisDrone dataset. Note that we select this dataset for augmentation, since it bears resemblances to the Fisheye8K dataset. Furthermore, we leverage pseudo labels generated by a pre-trained object detection model based on the Fisheye8K and original VisDrone dataset to further enrich the training data. Finally, we utilize various state-of-the-art object detection models trained with different combinations of the proposed augmented data, which are then combined with robust ensemble techniques to further enhance the overall object detection performance. As a result, our proposed method achieves a final F1 score of 64.06% on the 2024 AI City Challenge - Track 4 and ranks first among the competing teams.